What linguists always wanted to know about german and did not know how to estimate

This paper profiles significant differences in syntactic distribution and differences in word class frequencies for two treebanks of spoken and written German: the TüBa-D/S, a treebank of transliterated spontaneous dialogues, and the TüBa-D/Z treebank of newspaper articles published in the German daily newspaper die tageszeitung´(taz). The approach can be used more generally as a means of distinguishing and classifying language corpora of different genres

Treebank profiling of spoken and written German

This paper profiles significant differences in syntactic distribution and differences in word class frequencies for two treebanks of spoken and written German: the TüBa-D/S, a treebank of transliterated spontaneous dialogs, and the TüBa-D/Z treebank of newspaper articles published in the German daily newspaper ´die tageszeitung´(taz). The approach can be used more generally as a means of distinguishing and classifying language corpora of different genres

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Beyond Discourse: Computational Text Analysis and Material Historical Processes

This dissertation proposes a general methodological framework for the application of computational text analysis to the study of long duration material processes of transformation, beyond their traditional application to the study of discourse and rhetorical action. Over a thin theory of the linguistic nature of social facts, the proposed methodology revolves around the compilation of term co-occurrence matrices and their projection into different representations of an hypothetical semantic space. These representations offer solutions to two problems inherent to social scientific research: that of "mapping" features in a given representation to theoretical entities and that of "alignment" of the features seen in models built from different sources in order to enable their comparison. The data requirements of the exercise are discussed through the introduction of the notion of a "narrative horizon", the extent to which a given source incorporates a narrative account in its rendering of the context that produces it. Useful primary data will consist of text with short narrative horizons, such that the ideal source will correspond to a continuous archive of institutional, ideally bureaucratic text produced as mere documentation of a definite population of more or less stable and comparable social facts across a couple of centuries. Such a primary source is available in the Proceedings of the Old Bailey (POB), a collection of transcriptions of 197,752 criminal trials seen by the Old Bailey and the Central Criminal Court of London and Middlesex between 1674 and 1913 that includes verbatim transcriptions of witness testimony. The POB is used to demonstrate the proposed framework, starting with the analysis of the evolution of an historical corpus to illustrate the procedure by which provenance data is used to construct longitudinal and cross-sectional comparisons of different corpus segments. The co-occurrence matrices obtained from the POB corpus are used to demonstrate two different projections: semantic networks that model different notions of similarity between the terms in a corpus' lexicon as an adjacency matrix describing a graph and semantic vector spaces that approximate a lower-dimensional representation of an hypothetical semantic space from its empirical effects on the co-occurrence matrix. Semantic networks are presented as discrete mathematical objects that offer a solution to the mapping problem through operation that allow for the construction of sets of terms over which an order can be induced using any measure of significance of the strength of association between a term set and its elements. Alignment can then be solved through different similarity measures computed over the intersection and union of the sets under comparison. Semantic vector spaces are presented as continuous mathematical objects that offer a solution to the mapping problem in the linear structures contained in them. This include, in all cases, a meaningful metric that makes it possible to define neighbourhoods and regions in the semantic space and, in some cases, a meaningful orientation that makes it possible to trace dimensions across them. Alignment can then proceed endogenously in the case of oriented vector spaces for relative comparisons, or through the construction of common basis sets for non-oriented semantic spaces for absolute comparisons. The dissertation concludes with the proposition of a general research program for the systematic compilation of text distributional patterns in order to facilitate a much needed process of calibration required by the techniques discussed in the previous chapters. Two specific avenues for further research are identified. First, the development of incremental methods of projection that allow a semantic model to be updated as new observations come along, an area that has received considerable attention from the field of electronic finance and the pervasive use of Gentleman's algorithm for matrix factorisation. Second, the development of additively decomposable models that may be combined or disaggregated to obtain a similar result to the one that would have been obtained had the model being computed from the union or difference of their inputs. This is established to be dependent on whether the functions that actualise a given model are associative under addition or not